A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks

TL;DR

This paper introduces a scalable convex training framework for neural networks that enforces Lipschitz constraints, improving robustness and efficiency in safety-critical applications.

Contribution

It proposes a novel convex relaxation and a randomized subspace approach to efficiently enforce Lipschitz constraints during training.

Findings

Achieves competitive accuracy on MNIST, CIFAR-10, ImageNet

Provides significantly tighter Lipschitz bounds

Demonstrates improved training scalability and runtime

Abstract

Certified robustness is a critical property for deploying neural networks (NN) in safety-critical applications. A principle approach to achieving such guarantees is to constrain the global Lipschitz constant of the network. However, accurate methods for Lipschitz-constrained training often suffer from non-convex formulations and poor scalability due to reliance on global semidefinite programs (SDPs). In this letter, we propose a convex training framework that enforces global Lipschitz constraints via semidefinite relaxation. By reparameterizing the NN using loop transformation, we derive a convex admissibility condition that enables tractable and certifiable training. While the resulting formulation guarantees robustness, its scalability is limited by the size of global SDP. To overcome this, we develop a randomized subspace linear matrix inequalities (RS-LMI) approach that decomposes the global constraints into sketched layerwise constraints projected onto low-dimensional subspaces, yielding a smooth and memory-efficient training objective. Empirical results on MNIST, CIFAR-10, and ImageNet demonstrate that the proposed framework achieves competitive accuracy with significantly improved Lipschitz bounds and runtime performance.